Search (2500 results, page 3 of 125)

0.07128276 = product of:
  0.14256552 = sum of:
    0.14256552 = product of:
      0.28513104 = sum of:
        0.28513104 = weight(_text_:mining in 3822) [ClassicSimilarity], result of:
          0.28513104 = score(doc=3822,freq=8.0), product of:
            0.28585905 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.05066224 = queryNorm
            0.9974533 = fieldWeight in 3822, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.0625 = fieldNorm(doc=3822)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The volume presents 26 revised papers corresponding to the oral presentations given at the conference, also included are refereed papers corresponding to the 30 poster presentations. These papers were selected from a total of 73 full draft submissions. The papers are organized in topical sections on rule evaluation, visualization, association rules and text mining, KDD process and software, tree construction, sequential and spatial data mining, and attribute selection

Theme

Data Mining

0.07128276 = product of:
  0.14256552 = sum of:
    0.14256552 = product of:
      0.28513104 = sum of:
        0.28513104 = weight(_text_:mining in 4544) [ClassicSimilarity], result of:
          0.28513104 = score(doc=4544,freq=2.0), product of:
            0.28585905 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.05066224 = queryNorm
            0.9974533 = fieldWeight in 4544, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.125 = fieldNorm(doc=4544)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Theme

Data Mining

0.07128276 = product of:
  0.14256552 = sum of:
    0.14256552 = product of:
      0.28513104 = sum of:
        0.28513104 = weight(_text_:mining in 354) [ClassicSimilarity], result of:
          0.28513104 = score(doc=354,freq=32.0), product of:
            0.28585905 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.05066224 = queryNorm
            0.9974533 = fieldWeight in 354, product of:
              5.656854 = tf(freq=32.0), with freq of:
                32.0 = termFreq=32.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.03125 = fieldNorm(doc=354)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Web mining aims to discover useful information and knowledge from the Web hyperlink structure, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semistructured and unstructured nature of the Web data and its heterogeneity. It has also developed many of its own algorithms and techniques. Liu has written a comprehensive text on Web data mining. Key topics of structure mining, content mining, and usage mining are covered both in breadth and in depth. His book brings together all the essential concepts and algorithms from related areas such as data mining, machine learning, and text processing to form an authoritative and coherent text. The book offers a rich blend of theory and practice, addressing seminal research ideas, as well as examining the technology from a practical point of view. It is suitable for students, researchers and practitioners interested in Web mining both as a learning text and a reference book. Lecturers can readily use it for classes on data mining, Web mining, and Web search. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.

RSWK

World Wide Web / Data Mining

Subject

World Wide Web / Data Mining

Theme

Data Mining

0.07072368 = product of:
  0.14144737 = sum of:
    0.14144737 = product of:
      0.28289473 = sum of:
        0.28289473 = weight(_text_:mining in 1303) [ClassicSimilarity], result of:
          0.28289473 = score(doc=1303,freq=14.0), product of:
            0.28585905 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.05066224 = queryNorm
            0.9896301 = fieldWeight in 1303, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.046875 = fieldNorm(doc=1303)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The ferst part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programmeng; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining. This book will become a valuable source of reference for R&D professionals active in relational data mining. Students as well as IT professionals and ambitioned practitioners interested in learning about relational data mining will appreciate the book as a useful text and gentle introduction to this exciting new field.

Theme

Data Mining

0.07072368 = product of:
  0.14144737 = sum of:
    0.14144737 = product of:
      0.28289473 = sum of:
        0.28289473 = weight(_text_:mining in 4296) [ClassicSimilarity], result of:
          0.28289473 = score(doc=4296,freq=14.0), product of:
            0.28585905 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.05066224 = queryNorm
            0.9896301 = fieldWeight in 4296, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.046875 = fieldNorm(doc=4296)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Data mining (DM) is a multistaged process of extracting previously unanticipated knowledge from large databases, and applying the results to decision making. Data mining tools detect patterns from the data and infer associations and rules from them. The extracted information may then be applied to prediction or classification models by identifying relations within the data records or between databases. Those patterns and rules can then guide decision making and forecast the effects of those decisions. However, this definition may be applied equally to "knowledge discovery in databases" (KDD). Indeed, in the recent literature of DM and KDD, a source of confusion has emerged, making it difficult to determine the exact parameters of both. KDD is sometimes viewed as the broader discipline, of which data mining is merely a component-specifically pattern extraction, evaluation, and cleansing methods (Raghavan, Deogun, & Sever, 1998, p. 397). Thurasingham (1999, p. 2) remarked that "knowledge discovery," "pattern discovery," "data dredging," "information extraction," and "knowledge mining" are all employed as synonyms for DM. Trybula, in his ARIST chapter an text mining, observed that the "existing work [in KDD] is confusing because the terminology is inconsistent and poorly defined.

Theme

Data Mining

0.07072368 = product of:
  0.14144737 = sum of:
    0.14144737 = product of:
      0.28289473 = sum of:
        0.28289473 = weight(_text_:mining in 602) [ClassicSimilarity], result of:
          0.28289473 = score(doc=602,freq=14.0), product of:
            0.28585905 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.05066224 = queryNorm
            0.9896301 = fieldWeight in 602, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.046875 = fieldNorm(doc=602)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

We present an approach to enhancing information access through Web structure mining in contrast to traditional approaches involving usage mining. Specifically, we mine the hardwired hierarchical hyperlink structure of Web sites to identify patterns of term-term co-occurrences we call Web functional dependencies (FDs). Intuitively, a Web FD x -> y declares that all paths through a site involving a hyperlink labeled x also contain a hyperlink labeled y. The complete set of FDs satisfied by a site help characterize (flexible and expressive) interaction paradigms supported by a site, where a paradigm is the set of explorable sequences therein. We describe algorithms for mining FDs and results from mining several hierarchical Web sites and present several interface designs that can exploit such FDs to provide compelling user experiences.

Footnote

Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"

Theme

Data Mining

0.07072368 = product of:
  0.14144737 = sum of:
    0.14144737 = product of:
      0.28289473 = sum of:
        0.28289473 = weight(_text_:mining in 1497) [ClassicSimilarity], result of:
          0.28289473 = score(doc=1497,freq=14.0), product of:
            0.28585905 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.05066224 = queryNorm
            0.9896301 = fieldWeight in 1497, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.046875 = fieldNorm(doc=1497)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Text mining is about making serendipity more likely. Serendipity, the chance discovery of interesting ideas, has been responsible for many discoveries in science. Text mining systems strive to explore large text collections, separate the potentially meaningfull connections from a vast and mostly noisy background of random associations. In this paper we provide a summary of our text mining approach and also illustrate briefly some of the experiments we have conducted with this approach. In particular we use a profile-based text mining method. We have used these profiles to explore the global distribution of disease research, replicate discoveries made by others and propose new hypotheses. Text mining holds much potential that has yet to be tapped.

Theme

Data Mining

0.069734484 = product of:
  0.13946897 = sum of:
    0.13946897 = product of:
      0.27893794 = sum of:
        0.27893794 = weight(_text_:mining in 2910) [ClassicSimilarity], result of:
          0.27893794 = score(doc=2910,freq=10.0), product of:
            0.28585905 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.05066224 = queryNorm
            0.97578835 = fieldWeight in 2910, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2910)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Examines basic issues of data mining using the theory of rough sets, which is a recent proposal for generalizing classical set theory. The Pawlak rough set model is based on the concept of an equivalence relation. A generalized rough set model need not be based on equivalence relation axioms. The Pawlak rough set model has been used for deriving deterministic as well as probabilistic rules froma complete database. Demonstrates that a generalised rough set model can be used for generating rules from incomplete databases. These rules are based on plausability functions proposed by Shafer. Discusses the importance of rule extraction from incomplete databases in data mining

Footnote

Contribution to a special issue devoted to knowledge discovery and data mining

Theme

Data Mining

0.06705422 = product of:
  0.13410844 = sum of:
    0.13410844 = product of:
      0.4023253 = sum of:
        0.4023253 = weight(_text_:3a in 1826) [ClassicSimilarity], result of:
          0.4023253 = score(doc=1826,freq=2.0), product of:
            0.429515 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.05066224 = queryNorm
            0.93669677 = fieldWeight in 1826, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.078125 = fieldNorm(doc=1826)
      0.33333334 = coord(1/3)
  0.5 = coord(1/2)

Source

http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CDQQFjAE&url=http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F3131107&ei=HzFWVYvGMsiNsgGTyoFI&usg=AFQjCNE2FHUeR9oQTQlNC4TPedv4Mo3DaQ&sig2=Rlzpr7a3BLZZkqZCXXN_IA&bvm=bv.93564037,d.bGg&cad=rja

0.06682759 = product of:
  0.13365518 = sum of:
    0.13365518 = product of:
      0.26731035 = sum of:
        0.26731035 = weight(_text_:mining in 5653) [ClassicSimilarity], result of:
          0.26731035 = score(doc=5653,freq=18.0), product of:
            0.28585905 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.05066224 = queryNorm
            0.9351125 = fieldWeight in 5653, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5653)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Discusses the importance of knowledge organization in the context of the information overload caused by the vast quantities of data and information accessible on internal and external networks of an organization. Defines the characteristics of a knowledge-based product. Elaborates on the techniques and applications of text mining in developing knowledge products. Presents two approaches, as case studies, to the making of knowledge products: (1) steps and processes in the planning, designing and development of a composite multilingual multimedia CD product, with the potential international, inter-cultural end users in view, and (2) application of natural language processing software in text mining. Using a text mining software, it is possible to link concept terms from a processed text to a related thesaurus, glossary, schedules of a classification scheme, and facet structured subject representations. Concludes that the products of text mining and data mining could be made more useful if the features of a faceted scheme for subject classification are incorporated into text mining techniques and products.

Theme

Data Mining

0.06682759 = product of:
  0.13365518 = sum of:
    0.13365518 = product of:
      0.26731035 = sum of:
        0.26731035 = weight(_text_:mining in 804) [ClassicSimilarity], result of:
          0.26731035 = score(doc=804,freq=18.0), product of:
            0.28585905 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.05066224 = queryNorm
            0.9351125 = fieldWeight in 804, product of:
              4.2426405 = tf(freq=18.0), with freq of:
                18.0 = termFreq=18.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.0390625 = fieldNorm(doc=804)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory. As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments. This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.

LCSH

Data mining ; Information retrieval
Data mining / Congresses (GBV)

RSWK

Text Mining / Aufsatzsammlung

Subject

Text Mining / Aufsatzsammlung
Data mining ; Information retrieval
Data mining / Congresses (GBV)

Theme

Data Mining

0.062372416 = product of:
  0.12474483 = sum of:
    0.12474483 = product of:
      0.24948967 = sum of:
        0.24948967 = weight(_text_:mining in 69) [ClassicSimilarity], result of:
          0.24948967 = score(doc=69,freq=2.0), product of:
            0.28585905 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.05066224 = queryNorm
            0.8727716 = fieldWeight in 69, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.109375 = fieldNorm(doc=69)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

0.062372416 = product of:
  0.12474483 = sum of:
    0.12474483 = product of:
      0.24948967 = sum of:
        0.24948967 = weight(_text_:mining in 2300) [ClassicSimilarity], result of:
          0.24948967 = score(doc=2300,freq=8.0), product of:
            0.28585905 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.05066224 = queryNorm
            0.8727716 = fieldWeight in 2300, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2300)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

State of the art review of the recently developed concepts of data mining (defined as the automated process of evaluating data and finding relationships) and knowledge discovery (defined as the automated process of extracting information, especially unpredicted relationships or previously unknown patterns among the data) with particular reference to numerical data. Includes: the knowledge acquisition process; data mining; evaluation methods; and knowledge discovery. Concludes that existing work in the field are confusing because the terminology is inconsistent and poorly defined. Although methods are available for analyzing and cleaning databases, better coordinated efforts should be directed toward providing users with improved means of structuring search mechanisms to explore the data for relationships

Theme

Data Mining

0.062372416 = product of:
  0.12474483 = sum of:
    0.12474483 = product of:
      0.24948967 = sum of:
        0.24948967 = weight(_text_:mining in 3821) [ClassicSimilarity], result of:
          0.24948967 = score(doc=3821,freq=8.0), product of:
            0.28585905 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.05066224 = queryNorm
            0.8727716 = fieldWeight in 3821, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3821)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The 29 revised full papers presented together with 37 short papers were carefully selected from a total of 158 submissions. The book is divided into sections on emerging KDD technology; association rules; feature selection and generation; mining in semi-unstructured data; interestingness, surprisingness, and exceptions; rough sets, fuzzy logic, and neural networks; induction, classification, and clustering; visualization, causal models and graph-based methods; agent-based and distributed data mining; and advanced topics and new methodologies

Theme

Data Mining

0.062372416 = product of:
  0.12474483 = sum of:
    0.12474483 = product of:
      0.24948967 = sum of:
        0.24948967 = weight(_text_:mining in 3835) [ClassicSimilarity], result of:
          0.24948967 = score(doc=3835,freq=2.0), product of:
            0.28585905 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.05066224 = queryNorm
            0.8727716 = fieldWeight in 3835, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.109375 = fieldNorm(doc=3835)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Theme

Data Mining

0.062372416 = product of:
  0.12474483 = sum of:
    0.12474483 = product of:
      0.24948967 = sum of:
        0.24948967 = weight(_text_:mining in 2584) [ClassicSimilarity], result of:
          0.24948967 = score(doc=2584,freq=2.0), product of:
            0.28585905 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.05066224 = queryNorm
            0.8727716 = fieldWeight in 2584, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.109375 = fieldNorm(doc=2584)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

0.062372416 = product of:
  0.12474483 = sum of:
    0.12474483 = product of:
      0.24948967 = sum of:
        0.24948967 = weight(_text_:mining in 3905) [ClassicSimilarity], result of:
          0.24948967 = score(doc=3905,freq=8.0), product of:
            0.28585905 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.05066224 = queryNorm
            0.8727716 = fieldWeight in 3905, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3905)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

With increasing user presence in the Web and Web 2.0, Web social mining becomes an important and challenging task that finds a wide range of new applications relevant to e-commerce and social software. In this entry, we describe three Web social mining topics, namely, social network discovery, social network analysis, and social network applications. The essential concepts, models, and techniques of these Web social mining topics will be surveyed so as to establish the basic foundation for developing novel applications and for conducting research.

0.062372416 = product of:
  0.12474483 = sum of:
    0.12474483 = product of:
      0.24948967 = sum of:
        0.24948967 = weight(_text_:mining in 4104) [ClassicSimilarity], result of:
          0.24948967 = score(doc=4104,freq=8.0), product of:
            0.28585905 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.05066224 = queryNorm
            0.8727716 = fieldWeight in 4104, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4104)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

With the rapid development of Web 2.0, online reviews have become extremely valuable sources for mining customers' opinions. Fine-grained opinion mining has attracted more and more attention of both applied and theoretical research. In this article, the authors study how to automatically mine product features and opinions from multiple review sources. Specifically, they propose an integration strategy to solve the issue. Within the integration strategy, the authors mine domain knowledge from semistructured reviews and then exploit the domain knowledge to assist product feature extraction and sentiment orientation identification from unstructured reviews. Finally, feature-opinion tuples are generated. Experimental results on real-world datasets show that the proposed approach is effective.

Theme

Data Mining

0.062372416 = product of:
  0.12474483 = sum of:
    0.12474483 = product of:
      0.24948967 = sum of:
        0.24948967 = weight(_text_:mining in 4784) [ClassicSimilarity], result of:
          0.24948967 = score(doc=4784,freq=8.0), product of:
            0.28585905 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.05066224 = queryNorm
            0.8727716 = fieldWeight in 4784, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4784)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

LCSH

Data mining

RSWK

Data Mining / Kongress / Cochin <Kerala, 2010>

Subject

Data Mining / Kongress / Cochin <Kerala, 2010>
Data mining

0.061732687 = product of:
  0.123465374 = sum of:
    0.123465374 = product of:
      0.24693075 = sum of:
        0.24693075 = weight(_text_:mining in 2291) [ClassicSimilarity], result of:
          0.24693075 = score(doc=2291,freq=24.0), product of:
            0.28585905 = queryWeight, product of:
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.05066224 = queryNorm
            0.86381996 = fieldWeight in 2291, product of:
              4.8989797 = tf(freq=24.0), with freq of:
                24.0 = termFreq=24.0
              5.642448 = idf(docFreq=425, maxDocs=44218)
              0.03125 = fieldNorm(doc=2291)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This book offers a comprehensive introduction to the exploding field of data mining. We are surrounded by data, numerical and otherwise, which must be analyzed and processed to convert it into information that informs, instructs, answers, or otherwise aids understanding and decision-making. Due to the ever-increasing complexity and size of today's data sets, a new term, data mining, was created to describe the indirect, automatic data analysis techniques that utilize more complex and sophisticated tools than those which analysts used in the past to do mere data analysis. "Data Mining: Concepts, Models, Methods, and Algorithms" discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Detailed algorithms are provided with necessary explanations and illustrative examples. This text offers guidance: how and when to use a particular software tool (with their companion data sets) from among the hundreds offered when faced with a data set to mine. This allows analysts to create and perform their own data mining experiments using their knowledge of the methodologies and techniques provided. This book emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning. These critically important, qualitative decisions can only be made with the deeper understanding of parameter meaning and its role in the technique that is offered here. Data mining is an exploding field and this book offers much-needed guidance to selecting among the numerous analysis programs that are available.

LCSH

Data mining

RSWK

Data Mining / Lehrbuch

Subject

Data Mining / Lehrbuch
Data mining

Theme

Data Mining